Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,173 @@
-import altair as alt
+import json
+import joblib
 import numpy as np
 import pandas as pd
 import streamlit as st
+from pathlib import Path
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# -------------------------
+# Page config
+# -------------------------
+st.set_page_config(
+    page_title='Sales Forecast (LightGBM)',
+    page_icon='📈',
+    layout='centered'
+)
+
+st.title('📈 Sales Forecast (LightGBM)')
+st.write('Predict **num_sold** using a trained LightGBM model + saved encoders and preprocessing.')
+
+BASE_DIR = Path(__file__).resolve().parent
+
+MODEL_PATH = BASE_DIR / 'model_lgbm.pkl'
+FEATURES_PATH = BASE_DIR / 'feature_names.pkl'
+ENCODERS_PATH = BASE_DIR / 'encoders.pkl'
+FILLMAP_PATH = BASE_DIR / 'fill_map.pkl'
+META_PATH = BASE_DIR / 'meta.json'
+
+
+@st.cache_resource
+def load_assets():
+    if not MODEL_PATH.exists():
+        raise FileNotFoundError(f'Missing {MODEL_PATH.name} (put it next to app.py).')
+    if not FEATURES_PATH.exists():
+        raise FileNotFoundError(f'Missing {FEATURES_PATH.name} (put it next to app.py).')
+    if not ENCODERS_PATH.exists():
+        raise FileNotFoundError(f'Missing {ENCODERS_PATH.name} (put it next to app.py).')
+    if not FILLMAP_PATH.exists():
+        raise FileNotFoundError(f'Missing {FILLMAP_PATH.name} (put it next to app.py).')
+
+    model = joblib.load(MODEL_PATH)
+    features = joblib.load(FEATURES_PATH)
+    encoders = joblib.load(ENCODERS_PATH)
+    fill_map = joblib.load(FILLMAP_PATH)
+
+    meta = None
+    if META_PATH.exists():
+        with open(META_PATH, 'r') as f:
+            meta = json.load(f)
+
+    return model, features, encoders, fill_map, meta
+
+
+model, FEATURES, encoders, fill_map, meta = load_assets()
+
+with st.expander('ℹ️ Model info'):
+    if meta:
+        st.write(meta)
+    else:
+        st.write('No meta.json found.')
+
+
+# -------------------------
+# Helpers
+# -------------------------
+def make_date_features(date_value: pd.Timestamp) -> dict:
+    # date_value is a Timestamp
+    year = int(date_value.year)
+    month = int(date_value.month)
+    week = int(date_value.isocalendar().week)
+    dayofweek = int(date_value.dayofweek)  # Monday=0
+    is_weekend = int(dayofweek >= 5)
+    dayofyear = int(date_value.dayofyear)
+
+    return {
+        'year': year,
+        'month': month,
+        'week': week,
+        'dayofweek': dayofweek,
+        'is_weekend': is_weekend,
+        'dayofyear': dayofyear
+    }
+
+
+def safe_encode(col_name: str, value: str) -> int:
+    # If unseen label appears, fall back to the most frequent label (index 0) or safe default.
+    le = encoders.get(col_name)
+    if le is None:
+        return 0
+
+    classes = set(le.classes_.astype(str))
+    v = str(value)
+
+    if v in classes:
+        return int(le.transform([v])[0])
+
+    # fallback: use first known class
+    return int(le.transform([str(le.classes_[0])])[0])
+
+
+# -------------------------
+# UI Inputs
+# -------------------------
+st.subheader('🧾 Input')
+
+date_in = st.date_input('Date', value=pd.to_datetime('2019-01-01'))
+country_in = st.text_input('Country', value='Finland')
+store_in = st.text_input('Store', value='KaggleMart')
+product_in = st.text_input('Product', value='Kaggle Mug')
+
+st.markdown('---')
+st.subheader('⏳ Lag features')
+
+use_manual_lags = st.checkbox('Enter lag values manually (recommended if you know them)', value=False)
+
+default_lag_364 = float(fill_map.get('lag_364', 0.0))
+default_lag_365 = float(fill_map.get('lag_365', 0.0))
+default_lag_371 = float(fill_map.get('lag_371', 0.0))
+
+if use_manual_lags:
+    lag_364 = st.number_input('lag_364', value=default_lag_364)
+    lag_365 = st.number_input('lag_365', value=default_lag_365)
+    lag_371 = st.number_input('lag_371', value=default_lag_371)
+else:
+    st.write('Using default lag values (from training medians):')
+    st.write({
+        'lag_364': default_lag_364,
+        'lag_365': default_lag_365,
+        'lag_371': default_lag_371
+    })
+    lag_364, lag_365, lag_371 = default_lag_364, default_lag_365, default_lag_371
+
+
+# -------------------------
+# Predict
+# -------------------------
+if st.button('Predict'):
+    date_ts = pd.to_datetime(date_in)
+
+    d_feats = make_date_features(date_ts)
+
+    row = {}
+    row.update(d_feats)
+
+    row['lag_364'] = float(lag_364)
+    row['lag_365'] = float(lag_365)
+    row['lag_371'] = float(lag_371)
+
+    row['country'] = safe_encode('country', country_in)
+    row['store'] = safe_encode('store', store_in)
+    row['product'] = safe_encode('product', product_in)
+
+    X = pd.DataFrame([row])
+
+    # Ensure all FEATURES exist and order is correct
+    for c in FEATURES:
+        if c not in X.columns:
+            # numeric fallback from fill_map
+            X[c] = fill_map.get(c, 0.0)
+
+    X = X[FEATURES].copy()
+
+    # Fill numeric NaNs just in case
+    for c in X.columns:
+        if X[c].isna().any():
+            X[c] = X[c].fillna(fill_map.get(c, 0.0))
+
+    pred = model.predict(X)[0]
+
+    st.success(f'✅ Predicted num_sold: **{pred:.2f}**')
+    st.caption('Note: Lag features heavily influence the forecast. If you can compute real lags, the prediction will be more accurate.')
+
+    with st.expander('Show model input vector'):
+        st.dataframe(X)